Spraying is a critical surface treatment process in intelligent manufacturing, and coating quality directly affects product performance. Therefore, efficient, accurate, and intelligent coating defect detection is an essential technique to ensure product reliability. The past decade has witnessed rapid progress in coating defect detection techniques. However, most existing studies have focused on specific methods or application scenarios, and there is a lack of systematic reviews that provide a comprehensive overview of this particular research area. To fill this research gap, this paper systematically reviews recent advances in coating defect detection, which covers methods from physical property-based non-destructive testing to deep learning-based approaches. Their fundamental principles, applicability in intelligent manufacturing, and current research progress are examined, and key challenges and potential solutions are discussed. Furthermore, integration of advanced intelligent manufacturing technologies into coating defect detection systems is analyzed to enhance system-level digitalization, automation, and efficiency. Finally, future development trends are explored and analyzed, including collaborative perception, cross-modal fusion, and autonomous decision-making. It is expected that this review will help to advance and accelerate theoretical research and engineering applications in coating defect detection by providing researchers with a comprehensive understanding.